Efficient Recommendation of De-identification Policies using MapReduce - 2017


Abstract—Several information homeowners are required to release the data during a selection of world application, since it's of important importance to discovery valuable data keep behind the information. However, existing re-identification attacks on the AOL and ADULTS datasets have shown that publish such information directly may cause tremendous threads to the individual privacy. So, it's urgent to resolve all types of re-identification risks by recommending effective de-identification policies to ensure each privacy and utility of the info. De-identification policies is one of the models which will be used to realize such requirements, however, the amount of de-identification policies is exponentially massive because of the broad domain of quasi-identifier attributes. To higher management the trade off between data utility and information privacy, skyline computation will be used to pick such policies, however it's nonetheless difficult for efficient skyline processing over large number of policies. In this paper, we propose one parallel algorithm called SKY-FILTER-MR, which is predicated on MapReduce to beat this challenge by computing skylines over large scale de-identification policies that's represented by bit-strings. To additional improve the performance, a novel approximate skyline computation theme was proposed to prune unqualified policies using the approximately domination relationship. With approximate skyline, the facility of filtering in the policy space generation stage was greatly strengthened to effectively decrease the cost of skyline computation over alternative policies. Extensive experiments over both real life and artificial datasets demonstrate that our proposed SKY-FILTER-MR algorithm substantially outperforms the baseline approach by up to four times faster within the optimal case, which indicates smart scalability over large policy sets.

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